Proactive intention decoding remains a critical yet underexplored challenge in brain–machine interfaces (BMIs), especially under naturalistic, self-initiated behavior. Existing systems rely on reactive decoding of motor cortex signals, resulting in substantial latency. To address this, we leverage the common marmoset’s spontaneous vocalizations and develop a high-resolution, dual-region ECoG recording paradigm targeting the prefrontal and auditory cortices and a neural decoding framework that integrates shapelet-based temporal encoding, position-aware attention, frequency-aware channel masking, contrastive clustering and a minimum error entropy-based robust loss. Our approach achieves 91.9% accuracy up to 200 ms before vocal onset—substantially outperforming 13 competitive baselines. Our model also uncovers a functional decoupling between auditory and prefrontal regions. Furthermore, joint modeling in time and frequency domains reveals novel preparatory neural signatures preceding volitional vocal output. Together, our findings bridge the gap between foundational neuroscience and applied BMI engineering, and establish a generalizable framework for intention decoding from ecologically valid, asynchronous behaviors.

In recent years, deep multi-agent reinforcement learning (MARL) has demonstrated remarkable potential in solving complex cooperative tasks by enabling decentralized yet efficient coordination among agents. However, during decentralized training, agent policy updates induced by different joint action samples may conflict, leading to gradient interference that hinders convergence and the emergence of coordinated behavior. In this paper, we analyze and empirically validate the phenomenon of gradient interference. To address this, we then propose Gradient-Protected Value Decomposition (GPVD), a novel MARL framework that explicitly protects the gradient signals of optimal collaborative actions by suppressing the impact of interfering actions. GPVD employs a dynamic gradient protection mechanism that identifies optimal collaborative joint actions and reweights the loss to attenuate gradients from non-collaborative interfering actions. To effectively identify high-value collaborative actions, we apply SimHash-based state grouping to discover consistent collaboration patterns across similar states. Furthermore, a count-based intrinsic reward is incorporated to encourage exploration and improve the coverage of potentially optimal joint actions. Experiments on challenging multi-agent benchmarks demonstrate that GPVD achieves faster convergence, stronger coordination, and greater training stability compared to state-of-the-art value decomposition methods.

Brain disorders have been consistently associated with abnormalities in specific brain regions or neural circuits. Identifying key brain regional activities and functional connectivity patterns is essential for discovering more precise neurobiological biomarkers. However, previous studies have primarily emphasized alterations in functional connectivity while overlooking abnormal neuronal population activity within brain regions. To bridge this gap, we propose a novel Local-Global Coupling Spiking Graph Transformer (LGC-SGT) that jointly models both inter-regional connectivity differences and deviations in neuronal population firing rates within brain regions, enabling a dual-perspective neuropathological analysis. The global pathway leverages spike-based computation in LGC-SGT to model biologically plausible aberrant neural firing dynamics, while the local pathway adaptively captures abnormal graph-based representations of brain connectivity learned by local plasticity in the liquid state machine module. Furthermore, we design a shortcut-enhanced output strategy in LGC-SGT with the hybrid loss function to suppress outlier interference caused by inter-individual and inter-center variability, enabling a more robust decision boundary. Extensive experiments on three brain disorder datasets demonstrate that our model consistently outperforms state-of-the-art graph methods in brain disorder diagnosis. Moreover, it facilitates the extraction of interpretable neurobiological biomarkers by jointly analyzing regional neural activity and functional connectivity, offering a more comprehensive framework for brain disorder understanding and diagnosis.

Functional Magnetic Resonance Imaging (fMRI) presents challenges due to limited paired samples and low signal-to-noise ratios, particularly in tasks involving reconstructing natural images or decoding their semantic content. To address these challenges, we introduce BrainCLIP, an innovative fMRI-based brain decoding model. BrainCLIP leverages Contrastive Language-Image Pre-training's (CLIP) cross-modal generalization abilities to bridge brain activity, images, and text for the first time. Our experiments demonstrate CLIP's effectiveness in diverse brain decoding tasks, including zero-shot visual category decoding, fMRI-image/text alignment, and fMRI-to-image generation. The core objective of BrainCLIP is to train a mapping network that translates fMRI patterns into a unified CLIP embedding space, achieved through visual and textual supervision integration. Our experiments highlight that this approach significantly enhances performance in tasks such as fMRI-text alignment and fMRI-based image generation. Notably, BrainCLIP surpasses BraVL, a recent multi-modal method, in zero-shot visual category decoding. Moreover, BrainCLIP demonstrates strong capability in reconstructing visual stimuli with high semantic fidelity, competing favorably with state-of-the-art methods in capturing high-level semantic features during fMRI-based natural image reconstruction.

Through multiple levels of abstraction, deep learning takes advantage of multiple layers models to find the complicated structure and learn the high level representations of data. In recent years, deep learning has made great progress in object detection, speech recognition, and many other domains. The robustness of learning systems with deep architectures is however rarely studied and needs further investigation. Especially, the mean square error(MSE), which is commonly used as optimization cost function in deep learning, is sensitive to outliers(or impulsive noises). To combat the harmful influences caused by outliers which are pervasive in many real world data, it is indispensable to improve the robustness in deep learning. In this paper, a robust deep learning method based on generalized correntropy is proposed and named generaliezed correntropy induced loss function(GC-loss) based SAE(GC-SAE). Generalized correntropy as a nonlinear measure of similarity is robust to outliers and can approximate different norms(from l0to l2) of data. By using generalized Gaussian density(GGD) function as its kernel, generalized correntropy achieves a more flexible shape and shows a better robustness for non-Gaussian noise when compared with the original correntropy with Gaussian kernel. The good robustness of the proposed method is confirmed by the experiments on MNIST benchmark dataset.